Algebraic Curves That Work Better

Tolga Tasdizen, Jean-Philippe Tarel and David Cooper

Abstract

An algebraic curve is defined as the zero set of a polynomial in two variables. Algebraic curves are practical for modeling shapes much more complicated than conics or superquadrics. The main drawback in representing shapes by algebraic curves has been the lack of repeatability in fitting algebraic curves to data. A regularized fast linear fitting method based on ridge regression and restricting the representation to well behaved subsets of polynomials is proposed, and its properties are investigated. The fitting algorithm is of sufficient stability for very fast position-invariant shape recognition, position estimation, and shape tracking, based on new invariants and representations, and is appropriate to open as well as closed curves of unorganized data. Among appropriate applications are shape-based indexing into image databases.

Reference

@INPROCEEDINGS{jpt-cvpr99,
  author    = {Tasdizen, T. and Tarel, J.-P. and Cooper, D.B.},
  title     = {Algebraic Curves That Work Better},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'99)},  
  address   = {Fort Collins, Colorado, USA},
  date      = {June 23-25},
  volume    = {II},
  pages	    = {35-41},
  year	    = {1999},
  note      = {http://perso.lcpc.fr/tarel.jean-philippe/cvpr99.html}
}

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